Dependence of Cloud Feedback on the Spatial Pattern of Sea Surface Warming

An analysis with idealized experiments

Science

Lawrence Livermore National Laboratory scientists in the Atmosphere, Earth and Energy Division carried out a series of patch warming experiments to analyze the dependence of cloud feedback on the spatial pattern of sea surface warming. They show (a) positive global cloud feedback in response to warming in tropical subsidence regions and extratropical regions, (b) negative global cloud feedback in response to warming in tropical ascent regions, and (c) non-local effects of sea surface warming are important for cloud feedback.

Impact

This study explains how and why the spatial pattern of sea surface warming affects the magnitude of cloud feedback. This helps to reduce the uncertainty of cloud feedback and climate sensitivity.

Summary

The spatial pattern of sea surface temperature (SST) changes has a large impact on the magnitude of cloud feedback. In this study, we seek a basic understanding of the dependence of cloud feedback on the spatial pattern of warming. Idealized experiments are carried out with an AGCM to calculate the change in global mean cloud-induced radiation anomalies (∆Rcloud) in response to imposed surface warming/cooling in 74 individual localized oceanic “patches”. Then the cloud feedback in response to a specific warming pattern can be approximated as the superposition of global cloud feedback in response to a temperature change in each region, weighted by the magnitude of the local temperature changes. When there is a warming in the tropical subsidence or extratropical regions, the local decrease of LCC results in a positive change in Rcloud. Conversely, warming in tropical ascent regions increases the free-tropospheric temperature throughout the tropics, thereby enhancing the inversion strength over remote regions and inducing positive global low cloud cover (LCC) anomalies and negative Rcloud anomalies. The Green’s function approach performs reasonably well in predicting the response of global mean ∆LCC and net ∆Rcloud, but poorly for shortwave and longwave components of ∆Rcloud due to its ineffectiveness in predicting middle and high cloud cover changes. The approach successfully captures the change of cloud feedback in response to time-evolving CO2-induced warming, and captures the interannual variations in ∆Rcloud observed by CERES. The results highlight important non-local influences of SST changes on cloud feedback.